Introduction

In today's market, many companies have a mobile presence. Often these companies provide free products/services in their mobile apps in an attempt to transition their customers to a paid membership. Some examples of paid products, which originate from free ones, are YouTube Red, Pandora Premium, Netflix, Disney+Hotstar, AmazonPrime, Audible Subscription and Spotify. Since marketing efforts are never free, these companies need to know exactly who to target with offers and promotions.

  1. Market: The target audience is customers who use a company's free product. In this case study, this refers to users who installed (and used) the company's free mobile app.
  2. Product: The paid memberships often provide enhanced versions of the free products already given for free, alongside new features. For example, YouTube Red allows you to leave the app while still listening to a video.
  3. Goal : The objective of this model is to predict which users will not subscribe to the paid membership, so that greater marketing efforts can go into trying to 'convert' them to paid users.

Importing Essential Libraries and Our Data

As seen above, the hour column is not present. This is because it is of a string type. Now lets convert it to an int type.

Visualization

We will perform this on a copy of our dataset so that we don't end up messing our original dataset.

Feature Engineering

Response

Screen

Model Building

Results